Nucleic Acid Sensors and Programmed Cell Death
Bibliographic record
Abstract
Nucleic acids derived from microorganisms are powerful triggers for innate immune responses. Proteins called RNA and DNA sensors detect foreign nucleic acids and, in mammalian cells, include RIG-I, cGAS, and AIM2. On binding to nucleic acids, these proteins initiate signaling cascades that activate host defense responses. An important aspect of this defense program is the production of cytokines such as type I interferons and IL-1β. Studies conducted over recent years have revealed that nucleic acid sensors also activate programmed cell death pathways as an innate immune response to infection. Indeed, RNA and DNA sensors induce apoptosis, pyroptosis, and necroptosis. Cell death via these pathways prevents replication of pathogens by eliminating the infected cell and additionally contributes to the release of cytokines and inflammatory mediators. Interestingly, recent evidence suggests that programmed cell death triggered by nucleic acid sensors plays an important role in a number of noninfectious pathologies. In addition to nonself DNA and RNA from microorganisms, nucleic acid sensors also recognize endogenous nucleic acids, for example when cells are damaged by genotoxic agents and in certain autoinflammatory diseases. This review article summarizes current knowledge on the links between nucleic acid sensing and cell death and explores important open questions for future studies in this area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".